Software-Defined Networking (SDN) has evolved network management by detaching the control plane from the data forwarding plane, resulting in unparalleled flexibility and efficiency in network administration. However, the heterogeneity of traffic in SDN presents issues in achieving Quality of Service (QoS) demands and efficiently managing network resources. SDN traffic flows are often divided into elephant flows (EFs) and mice flows (MFs). EFs, which are distinguished by their huge packet sizes and long durations, account for a small amount of total traffic but require disproportionate network resources, thus causing congestion and delays for smaller MFs. MFs, on the other hand, have a short lifetime and are latency-sensitive, but they accou

Natural gas and oil are one of the mainstays of the global economy. However, many issues surround the pipelines that transport these resources, including aging infrastructure, environmental impacts, and vulnerability to sabotage operations. Such issues can result in leakages in these pipelines, requiring significant effort to detect and pinpoint their locations. The objective of this project is to develop and implement a method for detecting oil spills caused by leaking oil pipelines using aerial images captured by a drone equipped with a Raspberry Pi 4. Using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol, the acquired images and the global positioning system (GPS) coordinates of the images' acquisition are

The rapid development of Internet of Things (IoT) devices and their increasing numbers have caused a tremendous increase in network traffic and a wider range of cyber-attacks. This growing trend has complicated the detection process for traditional intrusion detection systems and heightened the challenges faced by these devices, such as imbalanced and large training data. This study presents a cohesive methodology of a series of intelligent techniques to prepare clean and balanced data for training the first (core) layer of a robust hierarchical intrusion detection system. The methodology was built by cleaning and compressing the data using an Autoencoder and preparing a strong latent space for balancing using a hybrid method that combines

With the vast usage of network services, Security became an important issue for all network types. Various techniques emerged to grant network security; among them is Network Intrusion Detection System (NIDS). Many extant NIDSs actively work against various intrusions, but there are still a number of performance issues including high false alarm rates, and numerous undetected attacks. To keep up with these attacks, some of the academic researchers turned towards machine learning (ML) techniques to create software that automatically predict intrusive and abnormal traffic, another approach is to utilize ML algorithms in enhancing Traditional NIDSs which is a more feasible solution since they are widely spread. To upgrade t

Abstract

The study aims to identify the reality of knowledge management in decision-making in academic departments from the point of view of faculty members at Blonde University. It also aims to propose mechanisms and suggestions to improve decision-making in the academic departments of the university in light of the management of knowledge. To achieve the objectives of the study, a descriptive method was used, in which a questionnaire consisted of (40) items administered to (137) participants of the faculty members. The results of the study indicated that the reality of the application of knowledge management in the decision-making process showed a high score mean.  There are no statistically significant d

THE EFFECT OF SPREACL of KNOWLEDGE ON ETHICS

This research aims to demonstrate the nature and concept of the Corona pandemic, its implications for the global economy, and the management and performance of companies in particular. Additionally, the research intends to measure the impact of the Corona pandemic on companies' financial performance. Listed on the Iraqi Stock Exchange, which has finished compiling its year-end financial statements for 2019-2020. The investigation arrived at several findings, the most significant of which was that most businesses were not prepared for such a crisis technologically or to develop human resources to deal with this pandemic. In addition, most companies experienced a decrease in their financial performance as a direct result of the Corona pandemi

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